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SemEval 2016: all accepted papers

212 papers · page 2 of 3 · the most cited first · ← SemEval 2016

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  1. JUNLP at SemEval-2016 Task 13: A Language Independent Approach for Hypernym Identification ↗Promita Maitra, Dipankar Das12 citations
  2. QASSIT at SemEval-2016 Task 13: On the integration of Semantic Vectors in Pretopological Spaces for Lexical Taxonomy Acquisition ↗Guillaume Cleuziou, Jose G. Moreno12 citations
  3. ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity ↗Junfeng Tian, Man Lan11 citations
  4. FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings ↗Duygu Ataman, José G. C. de Souza, Marco Turchi et al.11 citations
  5. ICL00 at SemEval-2016 Task 3: Translation-Based Method for CQA System ↗Yunfang Wu, Minghua Zhang11 citations
  6. INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification ↗Sebastian Ruder, Parsa Ghaffari, John G. Breslin11 citations
  7. M2L at SemEval-2016 Task 8: AMR Parsing with Neural Networks ↗Yevgeniy Puzikov, Daisuke Kawahara, Sadao Kurohashi11 citations
  8. NLDS-UCSC at SemEval-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in Tweets ↗Amita Misra, Brian Ecker, Theodore Handleman et al.11 citations
  9. NileTMRG at SemEval-2016 Task 7: Deriving Prior Polarities for Arabic Sentiment Terms ↗Samhaa R. El-Beltagy11 citations
  10. PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis ↗Mateusz Lango, Dariusz Brzezinski, Jerzy Stefanowski11 citations
  11. The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer ↗Johannes Bjerva, Johan Bos, Hessel Haagsma11 citations
  12. UFRGS&LIF at SemEval-2016 Task 10: Rule-Based MWE Identification and Predominant-Supersense Tagging ↗Silvio Cordeiro, Carlos Ramisch, Aline Villavicencio11 citations
  13. aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis ↗Stavros Giorgis, Apostolos Rousas, John Pavlopoulos et al.11 citations
  14. BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection ↗Jakub Macháček10 citations
  15. MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier ↗Shervin Malmasi, Marcos Zampieri10 citations
  16. Melbourne at SemEval 2016 Task 11: Classifying Type-level Word Complexity using Random Forests with Corpus and Word List Features ↗Julian Brooke, Alexandra Uitdenbogerd, Timothy Baldwin10 citations
  17. Pomona at SemEval-2016 Task 11: Predicting Word Complexity Based on Corpus Frequency ↗David Kauchak10 citations
  18. Sensible at SemEval-2016 Task 11: Neural Nonsense Mangled in Ensemble Mess ↗Gillin Nat10 citations
  19. UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound ↗James Goodman, Andreas Vlachos, Jason Naradowsky10 citations
  20. UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields ↗Mohammad Javad Hosseini, Noah A. Smith, Su-In Lee10 citations
  21. ECNU at SemEval-2016 Task 5: Extracting Effective Features from Relevant Fragments in Sentence for Aspect-Based Sentiment Analysis in Reviews ↗Mengxiao Jiang, Zhihua Zhang, Man Lan9 citations
  22. Garuda & Bhasha at SemEval-2016 Task 11: Complex Word Identification Using Aggregated Learning Models ↗Prafulla Choubey, Shubham Pateria9 citations
  23. ICL-HD at SemEval-2016 Task 10: Improving the Detection of Minimal Semantic Units and their Meanings with an Ontology and Word Embeddings ↗Angelika Kirilin, Felix Krauss, Yannick Versley9 citations
  24. KULeuven-LIIR at SemEval 2016 Task 12: Detecting Narrative Containment in Clinical Records ↗Artuur Leeuwenberg, Marie-Francine Moens9 citations
  25. ULISBOA at SemEval-2016 Task 12: Extraction of temporal expressions, clinical events and relations using IBEnt ↗Marcia Barros, Andre Lamurias, Gonçalo Figueiro et al.9 citations
  26. ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter ↗Yunxiao Zhou, Zhihua Zhang, Man Lan8 citations
  27. Inspire at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set Programming ↗Mishal Kazmi, Peter Schüller8 citations
  28. LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification ↗David Vilares, Yerai Doval, Miguel A. Alonso et al.8 citations
  29. Minions at SemEval-2016 Task 4: or how to build a sentiment analyzer using off-the-shelf resources? ↗Călin-Cristian Ciubotariu, Marius-Valentin Hrişca, Mihail Gliga et al.8 citations
  30. NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity ↗Kolawole John Adebayo, Luigi Di Caro, Guido Boella8 citations
  31. SentiSys at SemEval-2016 Task 5: Opinion Target Extraction and Sentiment Polarity Detection ↗Hussam Hamdan8 citations
  32. VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval ↗Tommaso Caselli, Roser Morante8 citations
  33. Amrita_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension ↗Barathi Ganesh HB, Anand Kumar M, Soman KP7 citations
  34. DTSim at SemEval-2016 Task 2: Interpreting Similarity of Texts Based on Automated Chunking, Chunk Alignment and Semantic Relation Prediction ↗Rajendra Banjade, Nabin Maharjan, Nobal Bikram Niraula et al.7 citations
  35. RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics ↗Ergun Biçici7 citations
  36. VRep at SemEval-2016 Task 1 and Task 2: A System for Interpretable Semantic Similarity ↗Sam Henry, Allison Sands7 citations
  37. CENTAL at SemEval-2016 Task 12: a linguistically fed CRF model for medical and temporal information extraction ↗Charlotte Hansart, Damien De Meyere, Patrick Watrin et al.6 citations
  38. FBK-HLT-NLP at SemEval-2016 Task 2: A Multitask, Deep Learning Approach for Interpretable Semantic Textual Similarity ↗Simone Magnolini, Anna Feltracco, Bernardo Magnini6 citations
  39. GTI at SemEval-2016 Task 4: Training a Naive Bayes Classifier using Features of an Unsupervised System ↗Jonathan Juncal-Martínez, Tamara Álvarez-López, Milagros Fernández-Gavilanes et al.6 citations
  40. GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS ↗Hanan Aldarmaki, Mona Diab6 citations
  41. LIMSI at SemEval-2016 Task 12: machine-learning and temporal information to identify clinical events and time expressions ↗Cyril Grouin, Véronique Moriceau6 citations
  42. NTNUSentEval at SemEval-2016 Task 4: Combining General Classifiers for Fast Twitter Sentiment Analysis ↗Brage Ekroll Jahren, Valerij Fredriksen, Björn Gambäck et al.6 citations
  43. SERGIOJIMENEZ at SemEval-2016 Task 1: Effectively Combining Paraphrase Database, String Matching, WordNet, and Word Embedding for Semantic Textual Similarity ↗Sergio Jimenez6 citations
  44. SentiSys at SemEval-2016 Task 4: Feature-Based System for Sentiment Analysis in Twitter ↗Hussam Hamdan6 citations
  45. TALN at SemEval-2016 Task 14: Semantic Taxonomy Enrichment Via Sense-Based Embeddings ↗Luis Espinosa-Anke, Francesco Ronzano, Horacio Saggion6 citations
  46. AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews ↗Shubham Pateria, Prafulla Choubey5 citations
  47. CLIP@UMD at SemEval-2016 Task 8: Parser for Abstract Meaning Representation using Learning to Search ↗Sudha Rao, Yogarshi Vyas, Hal Daumé III et al.5 citations
  48. DLS@CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity ↗Md Arafat Sultan, Steven Bethard, Tamara Sumner5 citations
  49. IIIT at SemEval-2016 Task 11: Complex Word Identification using Nearest Centroid Classification ↗Ashish Palakurthi, Radhika Mamidi5 citations
  50. IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner ↗Lavanya Tekumalla, Sharmistha Jat5 citations
  51. NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features ↗Piotr Przybyła, Nhung T. H. Nguyen, Matthew Shardlow et al.5 citations
  52. Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings ↗Hujie Wang, Pascal Poupart5 citations
  53. SentimentalITsts at SemEval-2016 Task 4: building a Twitter sentiment analyzer in your backyard ↗Cosmin Florean, Oana Bejenaru, Eduard Apostol et al.5 citations
  54. SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity ↗Peter Potash, William Boag, Alexey Romanov et al.5 citations
  55. USAAR at SemEval-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence Perplexity ↗José Manuel Martínez Martínez, Liling Tan5 citations
  56. iUBC at SemEval-2016 Task 2: RNNs and LSTMs for interpretable STS ↗Iñigo Lopez-Gazpio, Eneko Agirre, Montse Maritxalar5 citations
  57. ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity ↗Asli Eyecioglu, Bill Keller4 citations
  58. COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory ↗Kim Schouten, Flavius Frasincar4 citations
  59. Deftor at SemEval-2016 Task 14: Taxonomy enrichment using definition vectors ↗Hristo Tanev, Agata Rotondi4 citations
  60. DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons ↗Abeed Sarker, Graciela Gonzalez4 citations
  61. HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity ↗Matthias Liebeck, Philipp Pollack, Pashutan Modaresi et al.4 citations
  62. Hitachi at SemEval-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical Notes ↗Sarath P R, Manikandan R, Yoshiki Niwa4 citations
  63. IHS-RD-Belarus at SemEval-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of Sentence ↗Maryna Chernyshevich4 citations
  64. MayAnd at SemEval-2016 Task 5: Syntactic and word2vec-based approach to aspect-based polarity detection in Russian ↗Vladimir Mayorov, Ivan Andrianov4 citations
  65. OPAL at SemEval-2016 Task 4: the Challenge of Porting a Sentiment Analysis System to the “Real” World ↗Alexandra Balahur4 citations
  66. RDI_Team at SemEval-2016 Task 3: RDI Unsupervised Framework for Text Ranking ↗Ahmed Magooda, Amr Gomaa, Ashraf Mahgoub et al.4 citations
  67. RICOH at SemEval-2016 Task 1: IR-based Semantic Textual Similarity Estimation ↗Hideo Itoh4 citations
  68. SAARSHEFF at SemEval-2016 Task 1: Semantic Textual Similarity with Machine Translation Evaluation Metrics and (eXtreme) Boosted Tree Ensembles ↗Liling Tan, Carolina Scarton, Lucia Specia et al.4 citations
  69. SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification ↗Stefan Räbiger, Mishal Kazmi, Yücel Saygın et al.4 citations
  70. UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based Representation ↗Esteban Castillo, Ofelia Cervantes, Darnes Vilariño et al.4 citations
  71. UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED) ↗Jari Björne, Tapio Salakoski4 citations
  72. UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination ↗Ladislav Lenc, Pavel Král, Václav Rajtmajer4 citations
  73. YZU-NLP Team at SemEval-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional Network ↗Yunchao He, Liang-Chih Yu, Chin-Sheng Yang et al.4 citations
  74. BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content ↗Hao Wu, Heyan Huang, Wenpeng Lu3 citations
  75. DalGTM at SemEval-2016 Task 1: Importance-Aware Compositional Approach to Short Text Similarity ↗Jie Mei, Aminul Islam, Evangelos Milios3 citations
  76. Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction ↗Stefan Falk, Andi Rexha, Roman Kern3 citations
  77. NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library ↗Nikolay Karpov, Alexander Porshnev, Kirill Rudakov3 citations
  78. UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation ↗Milton King, Waseem Gharbieh, SoHyun Park et al.3 citations
  79. VectorWeavers at SemEval-2016 Task 10: From Incremental Meaning to Semantic Unit (phrase by phrase) ↗Andreas Scherbakov, Ekaterina Vylomova, Fei Liu et al.3 citations
  80. thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale ↗Vikrant Yadav3 citations
  81. DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity ↗Chris Hokamp, Piyush Arora2 citations
  82. DTSim at SemEval-2016 Task 1: Semantic Similarity Model Including Multi-Level Alignment and Vector-Based Compositional Semantics ↗Rajendra Banjade, Nabin Maharjan, Dipesh Gautam et al.2 citations
  83. DynamicPower at SemEval-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics core ↗Alastair Butler2 citations
  84. I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in Twitter ↗Zhengchen Zhang, Chen Zhang, Fuxiang Wu et al.2 citations
  85. IHS-RD-Belarus at SemEval-2016 Task 9: Transition-based Chinese Semantic Dependency Parsing with Online Reordering and Bootstrapping. ↗Artsiom Artsymenia, Palina Dounar, Maria Yermakovich2 citations
  86. INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words ↗Silvio Amir, Ramon F. Astudillo, Wang Ling et al.2 citations
  87. ISCAS_NLP at SemEval-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple Features ↗Cheng Fu, Bo An, Xianpei Han et al.2 citations
  88. NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity ↗John P. McCrae, Kartik Asooja, Nitish Aggarwal et al.2 citations
  89. OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency Parser ↗Lifeng Jin, Manjuan Duan, William Schuler2 citations
  90. Rev at SemEval-2016 Task 2: Aligning Chunks by Lexical, Part of Speech and Semantic Equivalence ↗Ping Tan, Karin Verspoor, Timothy Miller2 citations
  91. UMNDuluth at SemEval-2016 Task 14: WordNet's Missing Lemmas ↗Jon Rusert, Ted Pedersen2 citations
  92. VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter ↗Gerard Briones, Kasun Amarasinghe, Bridget McInnes2 citations
  93. WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models ↗Xin Tang, Fei Li, Donghong Ji2 citations
  94. mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter ↗Vittoria Cozza, Marinella Petrocchi2 citations
  95. DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach ↗Víctor Martinez Morant, LLuís-F. Hurtado, Ferran Pla1 citation
  96. Duluth at SemEval 2016 Task 14: Extending Gloss Overlaps to Enrich Semantic Taxonomies ↗Ted Pedersen1 citation
  97. PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks. ↗Uladzimir Sidarenka1 citation
  98. USFD at SemEval-2016 Task 1: Putting different State-of-the-Arts into a Box ↗Ahmet Aker, Frederic Blain, Andres Duque et al.1 citation
  99. UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation ↗Peng Li, Heng Huang1 citation
  100. UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis ↗Olga Vechtomova, Anni He1 citation

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